






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_albert import AlbertTokenizer
else:
      SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				None

SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__		:							List[Any] 				=				{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				{
    """albert-base-v1""": 5_12,
    """albert-large-v1""": 5_12,
    """albert-xlarge-v1""": 5_12,
    """albert-xxlarge-v1""": 5_12,
    """albert-base-v2""": 5_12,
    """albert-large-v2""": 5_12,
    """albert-xlarge-v2""": 5_12,
    """albert-xxlarge-v2""": 5_12,
}

SCREAMING_SNAKE_CASE__		:							str 				=				"""▁"""







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  VOCAB_FILES_NAMES
     __lowerCamelCase      				=  PRETRAINED_VOCAB_FILES_MAP
     __lowerCamelCase      				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowerCamelCase      				=  AlbertTokenizer




     def __init__(				self	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase=True	,						_lowerCAmelCase=True	,						_lowerCAmelCase=False	,						_lowerCAmelCase="[CLS]"	,						_lowerCAmelCase="[SEP]"	,						_lowerCAmelCase="<unk>"	,						_lowerCAmelCase="[SEP]"	,						_lowerCAmelCase="<pad>"	,						_lowerCAmelCase="[CLS]"	,						_lowerCAmelCase="[MASK]"	,						**_lowerCAmelCase	,						):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          UpperCAmelCase__      :  Dict				      =			(
              AddedToken(_lowerCAmelCase	,						lstrip=_lowerCAmelCase	,						rstrip=_lowerCAmelCase	,						normalized=_lowerCAmelCase		)
              if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		)
              else mask_token
          )

          super().__init__(
              _lowerCAmelCase	,						tokenizer_file=_lowerCAmelCase	,						do_lower_case=_lowerCAmelCase	,						remove_space=_lowerCAmelCase	,						keep_accents=_lowerCAmelCase	,						bos_token=_lowerCAmelCase	,						eos_token=_lowerCAmelCase	,						unk_token=_lowerCAmelCase	,						sep_token=_lowerCAmelCase	,						pad_token=_lowerCAmelCase	,						cls_token=_lowerCAmelCase	,						mask_token=_lowerCAmelCase	,						**_lowerCAmelCase	,						)

          UpperCAmelCase__      :  str				      =			do_lower_case
          UpperCAmelCase__      :  Union[str, Any]				      =			remove_space
          UpperCAmelCase__      :  List[str]				      =			keep_accents
          UpperCAmelCase__      :  Optional[Any]				      =			vocab_file
          UpperCAmelCase__      :  Optional[int]				      =			False if not self.vocab_file else True




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          UpperCAmelCase__      :  Optional[int]				      =			[self.sep_token_id]
          UpperCAmelCase__      :  str				      =			[self.cls_token_id]
          if token_ids_a is None:
               return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          UpperCAmelCase__      :  List[Any]				      =			[self.sep_token_id]
          UpperCAmelCase__      :  str				      =			[self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep		) * [0]
          return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          if not self.can_save_slow_tokenizer:
               raise ValueError(
                   """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                   """tokenizer."""		)

          if not os.path.isdir(_lowerCAmelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          UpperCAmelCase__      :  Dict				      =			os.path.join(
              _lowerCAmelCase	,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

          if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCAmelCase		):
               copyfile(self.vocab_file	,						_lowerCAmelCase		)

          return (out_vocab_file,)










def 							UpperCamelCase_(					_A :Union[str, Any]	)->		List[str]:
			UpperCamelCase__       =							[0] * len(_A	)
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							0

			for values in graph.values():
						for i in values:
									indegree[i] += 1

			for i in range(len(_A	)	):
						if indegree[i] == 0:
									queue.append(_A	)

			while queue:
						UpperCamelCase__       =							queue.pop(0	)
						cnt += 1
						topo.append(_A	)
						for x in graph[vertex]:
									indegree[x] -= 1
									if indegree[x] == 0:
												queue.append(_A	)

			if cnt != len(_A	):
						print("Cycle exists"	)
			else:
						print(_A	)


# Adjacency List of Graph
__UpperCamelCase     		=							{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


